Method and node for manufacturing a surgical kit for cartilage repair

ABSTRACT

A method of manufacturing a surgical kit for cartilage repair in an articulating surface of a joint, comprising the steps of receiving radiology image data representing three dimensional image of a joint; generating a first three dimensional representation of a first surface of the joint in a trainable image segmentation process dependent on a trained segmentation process control parameter set and said radiology image data; generating a set of data representing a geometrical object based on said first surface, wherein said geometrical object is confined by said first surface; generating control software adapted to control a CAD or CAM system to manufacture a surgical kit for cartilage repair dependent on said set of data representing a geometrical object and on a predetermined model of components of said surgical kit.

RELATED APPLICATION DATA

This application is a continuation application of pending prior U.S.patent application Ser. No. 15/417,657 (allowed), filed Jan. 27, 2017,and is a continuation application of prior U.S. patent application Ser.No. 15/116,829 (abandoned), filed Aug. 5, 2016, which is the NationalStage of International Application No. PCT/EP2014/052417 filed Feb. 7,2014, which are all herein incorporated in their entirety.

TECHNICAL FIELD

Generally, embodiments of the invention relate to the technical field ofdesigning and manufacturing customized implants used for patienttreatment in health care based on radiology imaging.

More specifically, different embodiments of the application relate to aprocess and a system for manufacturing a surgical kit for cartilagerepair in an articulating surface of a joint.

BACKGROUND

Radiology is a medical technique that uses imaging to diagnose and treatpatients for health related issues. An array of imaging techniques areused in radiology, like X-ray radiography, ultrasound, computedtomography (CT), nuclear medicine, positron emission tomography (PET)and magnetic resonance imaging (MRI).

The patient is exposed to radiology imaging technology by capturingthree dimensional radiology images, e.g. of a knee joint, that canprovide information about the human body's internal structures. Thisoccurs in a medical care facility, e.g. at a radiology department of ahospital. The images and the information obtained are forwarded to animplant design center for design of an implant and resulting controlsoftware (CAD/CAM), e.g. to improve or repair damaged cartilage, e.g. ina damaged human knee.

In conventional systems implants may be manufactured as surgical kits instandard sizes and might be supplied with standard guides to support inimplant surgery, e.g. to support in determining the position andmounting angle of the implant.

A problem with conventional systems is that implants are poorlycustomized to patients that may lead to replacement of unnecessary largeareas of undamaged cartilage bad alignment of the top surface of theimplant to the cartilage top surface being replaced, which in turn mightlead to reduced or no improvement of the condition of the person beingsubjected to implant surgery.

Another problem is that three dimensional representations of surfaces inthe body, e.g. a 3D surface derived from a radiology 3D image of anarticulating surface of a joint, that are generated based on threedimensional radiology images are generally not as smooth as the surfaceof healthy cartilage tissue.

Yet another problem is to generate or derive an accuratethree-dimensional representation of a surface of a joint based on 3Dradiology images and a segmentation process, wherein the segmentationprocess is controlled by a segmentation process control parameter set.

Yet another problem is to estimate an undamaged cartilage top surface inan area with damaged cartilage top surface by obtain a segmentationprocess control parameter set that will enable improved manufacturing ofa patient customized surgical kit for cartilage repair.

Therefore, there is a need for a system and method to improvemanufacturing of a surgical kit for cartilage repair.

OBJECT OF THE INVENTION

The object of the invention is to improve manufacturing of a patientcustomized surgical kit for cartilage repair.

SUMMARY

Embodiments of the present invention relate to improved manufacturing ofa surgical kit for cartilage repair. Certain embodiments includereceiving radiology image data representing three dimensional image of ajoint, generating a first three dimensional representation of a firstsurface of the joint in a trainable image segmentation process dependenton a trained segmentation process control parameter set and saidradiology image data, generating a second three dimensionalrepresentation of a second surface of the joint in a trainable dynamicalmodel process dependent on a trained dynamical model process controlparameter set and said radiology image data, generating a cartilagedamage perimeter CDP based on said radiology image data, generating aset of data representing a geometrical object based on said firstsurface, said second surface and said CDP, wherein said geometricalobject represent identified cartilage damage, wherein said geometricalobject is confined by said first surface, said second surface and saidCDP and generating control software adapted to control a CAD or CAMsystem to manufacture a surgical kit for cartilage repair dependent onsaid set of data representing a geometrical object and on apredetermined model of components of said surgical kit.

One of the advantages of the present disclosure is that improvedcustomization of a cartilage implant to a patient's body is achieved.

Another advantage of the present disclosure is that a smooth surfacealigning well with remaining healthy cartilage tissue is obtained afterimplant surgery.

Another advantage of the present disclosure is that an accuratethree-dimensional representation of a surface of a joint is obtained.

Another advantage of the current disclosure is that an improvedsegmentation process control parameter set is obtained, thereby enablingimproved manufacturing of a patient customized surgical kit forcartilage repair.

In one or more embodiments, a method of manufacturing a surgical kit forcartilage repair in an articulating surface of a joint, comprising thesteps of:

-   -   receiving radiology image data representing three dimensional        image of a joint;    -   generating a first three dimensional representation (330,460) of        a first surface of the joint (260) in a trainable image        segmentation process dependent on a trained segmentation process        control parameter set (420) and said radiology image data;    -   generating a set of data representing a geometrical object based        on said first surface, wherein said geometrical object is        confined by said first surface;    -   generating control software adapted to control a CAD or CAM        system to manufacture a surgical kit for cartilage repair        dependent on said set of data representing a geometrical object        and on a predetermined model of components of said surgical kit.

In one or more embodiments, further comprising the step generating asecond three dimensional representation (320) of a second surface of thejoint (230) in a trainable dynamical model process dependent on atrained dynamical model process control parameter set and said radiologyimage data; and;

generating a set of data representing a geometrical object further basedon said second three dimensional representation, wherein saidgeometrical object is further confined by said second surface;

In one or more embodiments, further comprising the step generating acartilage damage perimeter CDP based on said radiology image data; and;

generating a set of data representing a geometrical object further basedon said CDP, wherein said geometrical object is further confined by saidCDP;

In one or more embodiments, further comprising the step generating asurgical kit perimeter SKP based on said radiology image data; and;

generating a set of data representing a geometrical object further basedon said SKP, wherein said geometrical object is further confined by saidSKP;

In one or more embodiments, wherein generating a first three-dimensionalrepresentation of a first surface of the joint in a trainable imagesegmentation process further comprises the steps:

I) obtaining a predefined ordered set of segmentation process controlparameters instances;

II) generating a first three dimensional representation of said firstsurface based on the first instance of said trained segmentation processcontrol parameter set and said radiology image data;

III) store said first three-dimensional representation in a data buffer

IV) generating a first three dimensional representation 460 of saidfirst surface based on the next instance of said trained segmentationprocess control parameter set and said radiology image data;

V) store said first three-dimensional representation in a data buffer

VI) repeating steps IV and V for all instances of said predefinedordered set;

VII) determining an updated trained segmentation process controlparameter set based on a first three dimensional representation qualityvalue, wherein said first three dimensional representation quality valueis based on said three dimensional representations stored in the databuffer, said predefined ordered set and a predetermined object function.

In one or more embodiments, wherein generating a first three dimensionalrepresentation of a first surface of the joint in a trainable imagesegmentation process further comprises the steps:

XI) obtaining an initial segmentation process control parameter set;

XII) determining a trained segmentation process control parameter set assaid initial segmentation process control parameter set;

XIII) generating a first three dimensional representation of said firstsurface based on said trained segmentation process control parameter setand said radiology image data;

XIV) determining a differential trained segmentation process controlparameter set based on a first three dimensional representation qualityvalue, wherein said first three dimensional representation quality valueis based on said three dimensional representation and a predeterminedobject function.

XV) determining an updated trained segmentation process controlparameter set based on said trained segmentation process controlparameter set and said differential trained segmentation process controlparameter set

XVI) repeating steps XIII-XVI above if said first three dimensionalrepresentation quality value is below or above a predefined qualityvalue threshold.

In one or more embodiments, wherein generating a second threedimensional representation of a second surface of the joint in atrainable image segmentation process further comprises the steps:

XX) obtaining an initial dynamical model process control parameter set;

XXI) determining a trained dynamical model process control parameter setas said initial dynamical model process control parameter set;

XXII) generating a second three dimensional representation of saidsecond surface based on said trained dynamical model process controlparameter set and said radiology image data;

XXIII) determining a differential trained dynamical model processcontrol parameter set based on a three-dimensional representationquality value, wherein said three dimensional representation qualityvalue is based on said three-dimensional representation.

XXIV) determining an updated trained dynamical model process controlparameter set based on said trained dynamical model process controlparameter set and said differential trained dynamical model processcontrol parameter set;

XXV) repeating steps XX-XXV above if said three dimensionalrepresentation quality value is below or above a predefined qualityvalue threshold.

In one or more embodiments, wherein said radiology image data is basedon a selection of X-ray, ultrasound, computed tomography (CT), nuclearmedicine, positron emission tomography (PET) and magnetic resonanceimaging (MRI).

In one or more embodiments, an implant design center system formanufacturing a surgical kit for cartilage repair in an articulatingsurface of a joint, the system comprising:

a memory 830;

a communications interface 840;

a processor 810 configured to perform the steps of:

-   -   receiving radiology image data representing three dimensional        image of a joint;    -   generating a first three dimensional representation of a first        surface of the joint in a trainable image segmentation process        dependent on a trained segmentation process control parameter        set and said radiology image data;    -   generating a set of data representing a geometrical object based        on said first surface, wherein said geometrical object is        confined by said first surface;    -   generating control software adapted to control a CAD or CAM        system to manufacture a surgical kit for cartilage repair        dependent on said set of data representing a geometrical object        and on a predetermined model of components of said surgical kit.

A computer program product comprising computer readable code configuredto, when executed in a processor, perform any or all of the method stepsdescribed herein.

A non-transitory computer readable memory on which is stored computerreadable code configured to, when executed in a processor, perform anyor all of the method steps described herein.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the invention will now be described in more detail withreference to the appended drawings, wherein:

FIG. 1a illustrates healthy joint with bone and cartilage tissue.

FIG. 1b illustrates a damaged joint with bone and damaged cartilagetissue.

FIG. 2a illustrates a side view intersection of a generated firstthree-dimensional representation of a first surface of the joint, agenerated second three-dimensional representation of a second surface ofthe joint, a generated cartilage damage perimeter CDP and a generatedsurgical kit perimeter SKP in accordance with one or more embodiments ofthe present disclosure.

FIG. 2b illustrates a top view of a generated first three-dimensionalrepresentation of a first surface of the joint, a generated secondthree-dimensional representation of a second surface of the joint, agenerated cartilage damage perimeter CDP and a generated surgical kitperimeter SKP in accordance with one or more embodiments of the presentdisclosure.

FIG. 3a shows a schematic view of an embodiment of a first threedimensional representation of a first surface of the joint, a secondthree dimensional representation of a second surface of the joint and ageometrical object.

FIG. 3b shows a schematic view of an embodiment of an adapted implantcomponent comprised in a model of components of a surgical kit based ona geometrical object and an adapted implant guide component comprised ina model of components of a surgical kit based on a geometrical object.

FIG. 4 shows a schematic view of an embodiment of a trainable imagesegmentation process

FIG. 5 shows a schematic view of an alternative embodiment of atrainable image segmentation process

FIG. 6 shows a schematic view of an embodiment of a trained dynamicalmodel process

FIG. 7 shows a schematic view of an embodiment of a system formanufacturing a surgical kit for cartilage repair

FIG. 8 shows a schematic view of an embodiment of an implant designcenter adapted to generate control software adapted to control a CAD orCAM system to manufacture a surgical kit for cartilage repair.

FIG. 9 shows a flowchart of an embodiment of a computer-implementedmethod to manufacturing a surgical kit.

FIG. 10 shows a flowchart of yet an embodiment of a computer-implementedmethod to manufacturing a surgical kit for cartilage repair in anarticulating surface of a joint.

FIG. 11 shows a flowchart of yet an embodiment of a computer-implementedmethod to manufacturing a surgical kit for cartilage repair in anarticulating surface of a joint.

FIG. 12 shows a flowchart of yet an embodiment of a computer-implementedmethod to manufacturing a surgical kit for cartilage repair in anarticulating surface of a joint.

FIG. 13 shows a flowchart of yet an embodiment of a computer-implementedmethod to manufacturing a surgical kit for cartilage repair in anarticulating surface of a joint.

DETAILED DESCRIPTION Introduction

When repairing cartilage by performing cartilage implant surgery, afirst problem is to determine the design or dimensions of an implant.The implant is generally linked or attached to a patient's body byinserting an implant support into a drilled cavity, e.g. into the bone.Therefore yet another problem is to ensure that the cartilage implant isplaced and located correctly, i.e. that the position and mounting angleof the implant is correct, thereby aligning the outer or top surface ofthe implant to the remaining cartilage tissue.

The inventive concept described herein solves this by providing animplant and a matching implant guide, referred to as a surgical kit,based on radiology image data. The implant guide may be positioned andattached to the patient's body, typically the bone to which thecartilage is attached, thereby allowing a cavity to be drilled in thecorrect location and mounting angle for insertion of the implantsupport.

To customize the surgical kit to a particular patient the design of thesurgical kit is obtained by adapting a model of components, comprisingan implant component and an implant guide component. The outline of theimplant is determined based on a cartilage damage perimeter (CDP),indicating the extent of the cartilage damage, e.g. on a second surfaceof the joint. The outline of the implant guide is determined bygenerating a surgical kit perimeter (SKP), indicating the area on afirst surface of the joint, such as the underlying bone, available on aparticular patient to place an implant guide in. The outer or topsurface of the implant is determined by the remaining cartilage tissueouter surface and adapted so that a smooth transition between thecartilage outer surface and the implant outer surface is obtained, i.e.aligned to remaining cartilage tissue outer surface. The model ofcomponents is then adapted based on the first surface of the joint andalternatively the second surface of the joint, CDP and SKP.

To adapt the model components a first three dimensional representationof a first surface of the joint and optionally a second threedimensional representation of a second surface of the joint, CDP andoptionally SKP are generated based on radiology image data.

To extract the first surface from radiology image data, a firstthree-dimensional representation of a first surface may be generated byin an image segmentation process dependent on a segmentation processcontrol parameter set.

The outer or top surface of the implant is replacing damage tissue. Theouter or top surface of damaged tissue will not be identifiable in theradiology image data, however the undamaged tissue may be identified andused to adapt a dynamical model of a second surface to align with theundamaged tissue. Therefore, a second three dimensional representationof a second surface of the joint may be generated in a dynamical modelprocess dependent on a dynamical model process control parameter set.

Once the first three dimensional representation and optionally thesecond three dimensional representation, said CDP and said SKP have beengenerated, a set of data representing a geometrical object may begenerated and used to generate control software adapted to control a CADor CAM system to manufacture a surgical kit for cartilage repair.

Definitions

Surgical kit for cartilage repair is in this text to be understood as acustomized pair of implant and implant guide adapted to a person orpatient, scheduled for implant surgery, wherein the implant guide isconfigured to relieve and support the surgeon in implant surgery byguiding the implant to the wanted position and with the correct mountingangle on a person being subjected to implant surgery. If the implant isoffset from its intended position it may cause an increased wear or loadon the joint. A difficult task of a surgeon is to place or locate animplant. There is therefore a need for well-fitting implants as well astools that are designed to relieve and support the surgeon in implantsurgery. The design of the implant and the guide, in other words thedesign of the surgical kit, is crucial for the outcome of the implantslifetime in a patients joint. Small differences in the placement ordesign of the implant can make a huge difference in the benefit andlifetime of an implant in the patient's body, convalescence time for thepatient, economic values due to surgery time, success rate of implantsurgery.

Surface of a joint is in this text to be understood as a surfaceassociated to cartilage, such as the inner surface of the cartilageconnected to the bone or the outer surface of the cartilage.

Radiology image data representing a three dimensional image of a jointis in this text to be understood as data arranged in a three dimensionalarray of voxels or pixels or multiple ordered two dimensional imagesobtained at a predefined distance. The pixels may comprise pixel valuesof a predetermined resolution, e.g. 8, 16 or 32 bits, wherein the pixelvalues may indicate an intensity or greyscale value. In one example,radiology image data is in the form of a 3D array of voxels or pixels.Each voxel may have an intensity or greyscale value, e.g. in the rangefrom 0 to 65535 in the 16-bit pixel case or 0 to 255 in the 8-bit pixelcase. Most medical imaging systems generate images using 16-bitgreyscale range. A 3D image typically has a large number of pixels andis very computational intensive for processing such as segmentation andpattern recognition. To reduce complexity and computational intensitythree-dimensional representations of a first surface may be generated,e.g. in a process called segmentation.

Three-dimensional representation is in this text to be understood asdata values arranged in a data structure, such as an array, representinga three dimensional (3D) surface. Such a 3D surface may be a selectionof voxels from a 3D radiology image data, a three-dimensional mesh, aparametric surface, a surface model or any other representation of a 3Dsurface known to a person skilled in the art.

Trainable process is in this text to be understood as a process withassociated input data, such as image data, geometrical model and controlparameters, and output data, such as a three dimensional representation,adapted with a training unit configured to evaluate the output data bygenerating a quality value and modifying the input data based on thequality value in order to iterate the process. The iterations maycontinue until the quality value exceeds a predefined threshold value.

Image segmentation process is in this text to be understood as a processconfigured to partition image data in a semantically meaningful way togenerate a three dimensional representation, e.g. identifying anarticulating surface of a joint such as an inner surface of thecartilage connected to the bone, based on dynamical segmentation processcontrol parameters and radiology image data.

Segmentation process control parameter set is in this text to beunderstood as control parameters controlling the image segmentationprocess and therefore the properties of a three dimensionalrepresentation generated by the image segmentation process. Examples ofsuch parameters may be Desired hole radius, Number of classes toidentify as different regions or tissue types, Error tolerance forclustering, grayscale values into different classes, Degree ofsmoothness on the classified regions, Number of iterations, down samplefactor or Spline distance.

Dynamical model process is in this text to be understood as a processconfigured to adapt a dynamical model of a surface to align withpredetermined features of radiology image data, such as undamagedcartilage tissue along a perimeter or an estimated surface of undamagedcartilage, to generate a second three dimensional representation basedon dynamical model process control parameters and radiology image data.

Dynamical model process control parameter set is in this text to beunderstood as control parameters controlling the dynamical model processand thereby properties of the generated three-dimensionalrepresentation.

Perimeter is in this text to be understood as a perimeter delimiting asubset of a three dimensional representation.

Cartilage damage perimeter (CDP) is in this text to be understood as aperimeter delimiting a subset of a first or second three dimensionalrepresentation. In one example, this may comprise identifying theperimeter of an implant, part of a surgical kit, to be manufactured.

Surgical kit perimeter (SKP) is in this text to be understood as aperimeter delimiting a subset of a first three dimensionalrepresentation. In one example, this may comprise identifying theperimeter of possible locations of a guide, part of a surgical kit, tobe manufactured.

Geometrical object is in this text to be understood as a geometricalobject or volume delimited by a subset of the first three dimensionalrepresentation as the bottom, a subset of second three dimensionalrepresentation as the top and a surface interconnecting said top of thegeometrical object and said bottom of the geometrical object. The subsetof the first or second three-dimensional representation may be delimitedby a perimeter, such as the CDP or a surgical kit perimeter SKP.

Control software adapted to control a CAD or CAM system is in this textto be understood as data values represented by data structurescomprising computer program code portions configured to direct aprocessor to perform display or manufacturing of a surgical kit forcartilage repair in an articulating surface of a joint

Predetermined model of components is in this text to be understood as amodel comprising an implant component and an implant guide componentthat may be adapted based on a geometrical object, radiology image data,a first three dimensional representation and a second three dimensionalrepresentation. In one example, the implant component is adapted in sizeto replace damaged cartilage and the implant guide component is adaptedto fit on the first and second three-dimensional representation, toenable fixation points to the first surface or bone, to enable correctpositioning of the implant component and to enable the correct mountingangle for the implant component.

Three dimensional representation quality value is in this text to beunderstood as a quality value determined on a predetermined relationbased on a three dimensional representation. In one example, this mightinvolve comparing three-dimensional representation to a referencesurface, e.g. by generating a measure of distances between the surfacesin Euclidian space. In yet another example this might involve evaluatingthe smoothness of a three dimensional representation or the deviation ofa three dimensional representation compared to a reference surface.

DRAWINGS DESCRIPTIONS

FIG. 1a illustrates healthy joint 110 with bone 130 and cartilage tissue120.

FIG. 1b illustrates a damaged joint 140 with bone 150 and damagedcartilage tissue 160.

FIG. 2a illustrates a side view intersection of a generated first threedimensional representation of a first surface 260 of the joint, agenerated second three dimensional representation of a second surface ofthe joint 230, a generated cartilage damage perimeter CDP 250 and agenerated surgical kit perimeter SKP 270 in accordance with one or moreembodiments of the present disclosure. FIG. 2a further illustrates howsaid second three-dimensional representation of a second surface of thejoint 230 is aligned to remaining cartilage tissue outer surface.

FIG. 2b illustrates a top view of a generated first three dimensionalrepresentation of a first surface of the joint 260, a generatedcartilage damage perimeter CDP 250 and a generated surgical kitperimeter SKP 270 in accordance with one or more embodiments of thepresent disclosure.

FIG. 3a shows a schematic view of an embodiment of a generated firstthree-dimensional representation 330 of a first surface of the joint, agenerated second three-dimensional representation 320 of a secondsurface of the joint and a geometrical object 350. In one or moreembodiments the geometrical object is generated based on the CDP, thefirst three dimensional representation and the second three dimensionalrepresentation. In one or more embodiments said first three-dimensionalrepresentation 330 is represented in the form of selected voxels in theradiology image data, a polygon mesh, an anatomical model or aparametric surface.

In one non-limiting example an implant mounting position is determinedas a position on the first three dimensional representation comprised byCDP and an implant mounting angle is determined as the normal or surfacenormal vector of the first three dimensional representation. A firstsubset of said first three-dimensional representation is determinedbased on the CDP. The CDP is further translated along an axisrepresented by the implant mounting angle to the second threedimensional representation and second subset of said first threedimensional representation is determined based on the translated CDP. Ageometric object is then generated based on the first subset, the secondsubset and a surface interconnecting said first subset and secondsubset.

FIG. 3b illustrates a surgical kit manufactured based on controlsoftware adapted to control a CAD or CAM system to manufacture asurgical kit for cartilage repair dependent on a set of datarepresenting a geometrical object and on a predetermined model ofcomponents of said surgical kit according to the method of the presentdisclosure. In one or more embodiments, the predetermined model ofcomponents comprises an implant component 350 and an implant guidecomponent 360. In one or more embodiments, the predetermined model ofcomponents further comprises an implant support component 370. In one ormore embodiments, the implant component is adapted based on thegeometrical object. In one or more embodiments, the implant guidecomponent is adapted based on the geometrical object and SKP.

In one non-limiting example, the implant component is adapted by scalingand rotating the implant component so that it is comprised in saidgeometrical object and so that the top or outer surface of the implantcomponent is aligned with the geometrical object, thereby also aligningwith the undamaged cartilage tissue.

To design and manufacture a customized surgical kit for cartilage repairin an articulating surface of a joint it is necessary to identify areference surface, such as the bone underlying the cartilage tissue.This reference surface is identified by extracting a first surface fromradiology image data, e.g. by generating a first three dimensionalrepresentation of a first surface in a radiology image segmentationprocess dependent on a segmentation process control parameter set. Thequality and accuracy of this generated first three-dimensionalrepresentation or surface depend on the segmentation process controlparameter set and may vary for the individual patient and for individualtypes of joints, e.g. knee, toe, elbow etc. To further quality andaccuracy of this generated first three-dimensional representation, thesegmentation process control parameter set may be obtained throughparameter training. Parameter training generally comprise attempting tofit a certain model, e.g. an objective function, to observed data suchas a first three dimensional representation of a first surface.

FIG. 4 shows a schematic view of an embodiment of a method for atrainable image segmentation process according to the presentdisclosure. In one or more embodiment a first three dimensionalrepresentation 460 of a first surface of the joint is generated in atrainable image segmentation process 450 dependent on a trainedsegmentation process control parameter set 430 and radiology image data410. In one or more embodiments, the method further comprises:

I) obtaining a predefined ordered set of segmentation process controlparameters instances;

II) generating a first three dimensional representation 460 of saidfirst surface based on the first instance of said trained segmentationprocess control parameter set and said radiology image data;

III) store said first three-dimensional representation in a memory ordata buffer 480

IV) generating a first three dimensional representation 460 of saidfirst surface based on the next instance of said trained segmentationprocess control parameter set and said radiology image data;

V) store said first three-dimensional representation in a data buffer480

III) repeating steps I and II for all instances of said predefinedordered set;

IV) determining an updated trained segmentation process controlparameter set 490 based on a first three dimensional representationquality value, wherein said first three dimensional representationquality value is based on said three dimensional representations storedin the data buffer 480, said predefined ordered set and a predeterminedobject function.

In one non-limiting example, a predefined number of instances ofsegmentation process control parameters are obtained as an ordered set,e.g. ten previously used parameter sets used in a segmentation process.A first three dimensional representation is generated for each instanceof segmentation process control parameters and stored in a data bufferor memory. An object function, e.g. a measure of how a segmented surfacedeviate from a reference surface such as a parametric surface, is usedto determine a first three-dimensional representation quality value. Anupdated trained segmentation process control parameter set is thendetermined, e.g. by selecting an instance from the ordered set based onthe first three dimensional representation quality value, by combininginstances from the ordered set based on the first three dimensionalrepresentation quality value or to generate a new instance of a trainedsegmentation process control parameter set. In alternative embodiments,any objective function known to the skilled person may be used, such asroughness Measures for 3D surfaces/meshes.

FIG. 5 shows a schematic view of an embodiment of a trainable imagesegmentation process according to method of the present disclosure. Inone or more embodiment a first three dimensional representation 560 of afirst surface of the joint in a trainable image segmentation process 550dependent on a trained segmentation process control parameter set 530and radiology image data 510. In one or more embodiments, the methodfurther comprises:

I) obtaining an initial segmentation process control parameter set 520;

II) determining a trained segmentation process control parameter set 530as said initial segmentation process control parameter set 520;

III) generating a first three dimensional representation 560 of saidfirst surface based on said trained segmentation process controlparameter set and said radiology image data;

IV) determining 570 a differential trained segmentation process controlparameter set 540 based on a first three dimensional representationquality value, wherein said first three dimensional representationquality value is based on said three dimensional representation 560 anda predetermined object function.

V) determining an updated trained segmentation process control parameterset 530 based on said trained segmentation process control parameter set520 and said differential trained segmentation process control parameterset 540

VI) repeating steps III-VI above if said first three dimensionalrepresentation quality value is below or above a predefined qualityvalue threshold.

In one non-limiting example, an initial segmentation process controlparameter set is obtained, e.g. as previously used and storedsegmentation process control parameter set. The initial segmentationprocess control parameter set is determined as a trained segmentationprocess control parameter set and used together with received radiologyimage data, descriptive of a joint, in a trained segmentation process togenerate a first three dimensional representation. The generated firstthree-dimensional representation is evaluated by a predetermined objectfunction, e.g. a measure of how a segmented surface deviate from areference surface, to obtain a first three-dimensional representationquality value. A differential trained segmentation process controlparameter set is determined based on a first three dimensionalrepresentation quality value, e.g. by applying predetermined deltavalues to said trained segmentation process control parameter set or byperforming a lookup in a predetermined table comprising trainedsegmentation process control parameter sets and first three dimensionalrepresentation quality values. An updated trained segmentation processcontrol parameter set is further determined based on said differentialtrained segmentation process control parameter set and said trainedsegmentation process control parameter set. The method steps may then berepeated until it is determined that said first three-dimensionalrepresentation quality value is below, above or equal to a predefinedquality value threshold.

FIG. 6 shows a schematic view of an embodiment of a trainable dynamicalmodel process according to method of the present disclosure. In one ormore embodiments a second three dimensional representation 660 of asecond surface of the joint in a trainable dynamical model process 650dependent on a trained dynamical model process control parameter set 630and radiology image data 610. In one or more embodiments, the methodfurther comprises:

X) obtaining an initial dynamical model process control parameter set620;

XI) determining a trained dynamical model process control parameter set630 as said initial dynamical model process control parameter set 620;

XII) generating a second three dimensional representation 660 of saidsecond surface based on said trained dynamical model process controlparameter set and said radiology image data 610;

XIII) determining 670 a differential trained dynamical model processcontrol parameter set 640 based on a three-dimensional representationquality value, wherein said three dimensional representation qualityvalue is based on said three-dimensional representation 660.

XIV) determining an updated trained dynamical model process controlparameter set 630 based on said trained dynamical model process controlparameter set 620 and said differential trained dynamical model processcontrol parameter set 640;

XV) repeating steps X-XV above if said three dimensional representationquality value is below or above a predefined quality value threshold.

In one or more embodiments, wherein generating a second threedimensional representation is further based on a dynamical model, suchas an anatomical model or a parametric surface.

In one non-limiting example, an initial dynamical model process controlparameter set is obtained, e.g. as the previously used and storeddynamical model process control parameter set. The initial dynamicalmodel process control parameter set is determined as a trained dynamicalmodel process control parameter set and used together with receivedradiology image data, descriptive of a joint, in a trained dynamicalmodel process to generate a second three dimensional representation. Thegenerated second three-dimensional representation is evaluated by apredetermined object function, e.g. a measure of how the generatedsecond three-dimensional representation deviate from a referencesurface, to obtain a second three-dimensional representation qualityvalue. A differential trained dynamical model process control parameterset is determined based on a second three dimensional representationquality value, e.g. by applying predetermined delta values to saidtrained dynamical model process control parameter set or by performing alookup in a predetermined table comprising trained dynamical modelprocess control parameter sets and second three dimensionalrepresentation quality values. An updated trained dynamical modelprocess control parameter set is further determined based on saiddifferential trained dynamical model process control parameter set andsaid trained dynamical model process control parameter set. The methodsteps may then be repeated until it is determined that said secondthree-dimensional representation quality value is below, above or equalto a predefined quality value threshold.

FIG. 7 shows a schematic view of an embodiment of a system formanufacturing a surgical kit for cartilage repair. In one or moreembodiments the system comprises a diagnosis center 710 with a diagnosisprocessor 713 configured to accept user indications as diagnosis dataindicative of cartilage damage and patient 714 related information via auser input/output device 711. The diagnosis processor 713 is furtherconfigured to send the diagnosis data via a communications network 740to a radiology-imaging center 720. The radiology-imaging center 720further comprises an imaging processor 723 configured to presentdiagnosis data to a user, to obtain radiology image data from aradiology image device 724, e.g. a CT or MR scanner, and to senddiagnosis data and radiology image data as a signal via a communicationsnetwork 740 to an implant design center. The implant design centercomprises a processor 733 configured to receive diagnosis data andradiology image data and optionally store said diagnosis data andradiology image data to memory 732. The processor 733 is furtherconfigured to generating a first three dimensional representation of afirst surface of the joint in a trainable image segmentation processdependent on a trained segmentation process control parameter set andsaid radiology image data, generating a second three dimensionalrepresentation of a second surface of the joint in a trainable dynamicalmodel process dependent on a trained dynamical model process controlparameter set and said radiology image data; generating a cartilagedamage perimeter CDP based on said radiology image data, generating aset of data representing a geometrical object based on said firstsurface, said second surface and said CDP, wherein said geometricalobject represent identified cartilage damage, wherein said geometricalobject is confined by said first surface, said second surface and saidCDP, generating control software adapted to control a CAD or CAM systemto manufacture a surgical kit for cartilage repair dependent on said setof data representing a geometrical object and on a predetermined modelof components of said surgical kit. The processor 733 is furtherconfigured to send said control software to an implant production center740. The implant production center 740 comprises a processor 743configured to receive control software and optionally store saiddiagnosis data and radiology image data to memory 742. The processor 743is further configured to control a production line to manufacture asurgical kit for cartilage repair based on said received controlsoftware.

FIG. 8 shows a schematic view of an embodiment of an implant designcenter adapted to generate control software adapted to control a CAD orCAM system to manufacture a surgical kit for cartilage repair. In one ormore embodiments, an implant design center, e.g. in the form of a tabletcomputer, laptop computer or desktop computer. Said implant designcenter is configured for generating control software adapted to controla CAD or CAM system to manufacture a surgical kit for cartilage repair.The design center further comprises a processor/processing unit 810provided with specifically designed programming or program code portionsadapted to control the processing unit 810 to perform the steps andfunctions of embodiments of the methods described herein. The computersystem further comprises at least one memory 830 configured to storedata values or parameters received from the processor 810 or to retrieveand send data values or parameters to the processor 810. In one or moreembodiments, the design center further comprises a display configured toreceive a signal from the processor 810 and to display the receivedsignal as a displayed image, e.g. to a user of the design center. In oneor more embodiments, the design center further comprises a user inputdevice 825 configured to receive indications from a user and to generatedata indicative of user input, thereby enabling the user to interactwith the implant design center. The user input device 825 is furtherconfigured to send the generated data as a signal to said processor 810.In one or more embodiments computer system further comprises acommunications interface 840 configured to send or receive data valuesor parameters to/from a processor 810 to/from external units via thecommunications interface 840. In one or more embodiments thecommunications interface 840 is configured to communicate via acommunications network.

Further Embodiments

FIG. 9 shows a flowchart of a computer-implemented method tomanufacturing a surgical kit for cartilage repair in an articulatingsurface of a joint. In one or more embodiments, the method comprises thesteps of:

receiving 910 radiology image data representing three-dimensional imageof a joint;

generating 920 a first three dimensional representation of a firstsurface of the joint in a trainable image segmentation process dependenton a trained segmentation process control parameter set and saidradiology image data;

generating 950 a set of data representing a geometrical object based onsaid first surface, wherein said geometrical object is confined by saidfirst surface;

generating 970 control software adapted to control a CAD or CAM systemto manufacture a surgical kit for cartilage repair dependent on said setof data representing a geometrical object and on a predetermined modelof components of said surgical kit.

FIG. 10 shows a flowchart of a computer-implemented method tomanufacturing a surgical kit for cartilage repair in an articulatingsurface of a joint. In one or more embodiments, the method comprises thesteps of:

receiving 1010 radiology image data representing three-dimensional imageof a joint;

generating 1020 a first three dimensional representation of a firstsurface of the joint in a trainable image segmentation process dependenton a trained segmentation process control parameter set and saidradiology image data;

generating 1030 a second three dimensional representation of a secondsurface of the joint in a trainable dynamical model process dependent ona trained dynamical model process control parameter set and saidradiology image data;

generating 1060 a set of data representing a geometrical object based onsaid first surface, and said second surface, wherein said geometricalobject is confined by said first surface and said second;

generating 1070 control software adapted to control a CAD or CAM systemto manufacture a surgical kit for cartilage repair dependent on said setof data representing a geometrical object and on a predetermined modelof components of said surgical kit.

FIG. 11 shows a flowchart of a computer-implemented method tomanufacturing a surgical kit for cartilage repair in an articulatingsurface of a joint. In one or more embodiments, the method comprises thesteps of:

receiving 1110 radiology image data representing three-dimensional imageof a joint;

generating 1120 a first three dimensional representation of a firstsurface of the joint in a trainable image segmentation process dependenton a trained segmentation process control parameter set and saidradiology image data;

generating 1130 a second three dimensional representation of a secondsurface of the joint in a trainable dynamical model process dependent ona trained dynamical model process control parameter set and saidradiology image data;

generating 1140 a cartilage damage perimeter CDP based on said radiologyimage data;

generating 1160 a set of data representing a geometrical object based onsaid first surface, said second surface and said CDP, wherein saidgeometrical object represent identified cartilage damage, wherein saidgeometrical object is confined by said first surface, said secondsurface and said CDP;

generating 1170 control software adapted to control a CAD or CAM systemto manufacture a surgical kit for cartilage repair dependent on said setof data representing a geometrical object and on a predetermined modelof components of said surgical kit.

FIG. 12 shows a flowchart of a computer-implemented method tomanufacturing a surgical kit for cartilage repair in an articulatingsurface of a joint. In one or more embodiments, the method comprises thesteps of:

receiving 1210 radiology image data representing three-dimensional imageof a joint;

generating 1220 a first three dimensional representation of a firstsurface of the joint in a trainable image segmentation process dependenton a trained segmentation process control parameter set and saidradiology image data;

generating 1230 a second three dimensional representation of a secondsurface of the joint in a trainable dynamical model process dependent ona trained dynamical model process control parameter set and saidradiology image data;

generating 1250 a surgical kit perimeter SKP based on said radiologyimage data;

generating 1260 a set of data representing a geometrical object based onsaid first surface, said second surface and said SKP, wherein saidgeometrical object represent identified cartilage damage, wherein saidgeometrical object is confined by said first surface, said secondsurface and said SKP;

generating 1270 control software adapted to control a CAD or CAM systemto manufacture a surgical kit for cartilage repair dependent on said setof data representing a geometrical object and on a predetermined modelof components of said surgical kit.

FIG. 13 shows a flowchart of a computer-implemented method tomanufacturing a surgical kit for cartilage repair in an articulatingsurface of a joint. In one or more embodiments, the method comprises thesteps of:

receiving 1310 radiology image data representing three-dimensional imageof a joint;

generating 1320 a first three dimensional representation of a firstsurface of the joint in a trainable image segmentation process dependenton a trained segmentation process control parameter set and saidradiology image data;

generating 1330 a second three dimensional representation of a secondsurface of the joint in a trainable dynamical model process dependent ona trained dynamical model process control parameter set and saidradiology image data;

generating 1340 a cartilage damage perimeter CDP based on said radiologyimage data;

generating 1350 a surgical kit perimeter SKP based on said radiologyimage data;

generating 1360 a set of data representing a geometrical object based onsaid first surface, said second surface, said SKP and said CDP, whereinsaid geometrical object represent identified cartilage damage, whereinsaid geometrical object is confined by said first surface, said secondsurface, said SKP and said CDP;

generating 1370 control software adapted to control a CAD or CAM systemto manufacture a surgical kit for cartilage repair dependent on said setof data representing a geometrical object and on a predetermined modelof components of said surgical kit.

In one or more embodiments, the implant design center system comprises aprocessor unit (e.g., a processor, microcontroller, or other circuit orintegrated circuit capable of executing instructions to perform variousprocessing operations)

In one or more embodiments, an implant design center with non-transitorymachine-readable medium on which is stored machine-readable code that,when executed by a processor of a remote inspection system, cause theprocessor to perform the methods described herein.

In one or more embodiments, a computer program product comprisingcomputer readable code configured to, when executed in a processor,perform any or all of the method steps described herein.

Where applicable, various embodiments provided by the present disclosurecan be implemented using hardware, software, or combinations of hardwareand software. In addition, where applicable, the various hardwarecomponents and/or software components set forth herein can be combinedinto composite components comprising software, hardware, and/or bothwithout departing from the spirit of the present disclosure. Whereapplicable, the various hardware components and/or software componentsset forth herein can be separated into sub-components comprisingsoftware, hardware, or both without departing from the spirit of thepresent disclosure. In addition, where applicable, it is contemplatedthat software components can be implemented as hardware components, andvice-versa.

Software in accordance with the present disclosure, such asnon-transitory instructions, program code, and/or data, can be stored onone or more non-transitory machine-readable mediums. It is alsocontemplated that software identified herein can be implemented usingone or more general purpose or specific purpose computers and/orcomputer systems, networked and/or otherwise. Where applicable, theordering of various steps described herein can be changed, combined intocomposite steps, and/or separated into sub-steps to provide featuresdescribed herein.

Embodiments described above illustrate but do not limit the invention.It should also be understood that numerous modifications and variationsare possible in accordance with the principles of the invention.Accordingly, the scope of the invention is defined only by the followingclaims.

1. A method of generating a three dimensional representation of a joint,comprising: receiving radiology image data representing images of ajoint in three dimensions; obtaining an initial segmentation processcontrol parameter set as a previously used and stored trainedsegmentation process control parameter set; generating a first threedimensional representation based on the trained segmentation processcontrol parameter set; evaluating said generated first three dimensionalrepresentation by generating a quality value; modifying the trainedsegmentation process control parameter set based on said quality valuein order to iterate the process until said quality value exceeds apredefined threshold value; and generating a three dimensionalrepresentation of a first surface of the joint based on radiology imagedata and the trained segmentation process control parameter set whensaid quality value exceeds the predefined threshold value.
 2. The methodof claim 1, further comprising generating a set of data representing ageometrical object based on said first surface, wherein said geometricalobject is confined by said first surface.
 3. The method of claim 1,further comprising generating a second three dimensional representationof a second surface of the joint in a trainable dynamical model processdependent on a trained dynamical model process control parameter set andsaid radiology image data.
 4. The method of claim 3, further comprisinggenerating the set of data representing a geometrical object based onsaid first surface and said second surface, so that said geometricalobject is confined by said first surface and said second surface.
 5. Themethod of claim 3, wherein the first surface of the joint is theunderlying bone, and the second surface of the joint is aligned toundamaged cartilage tissue outer surface.
 6. The method of claim 2,further comprising generating a cartilage damage perimeter (CDP) basedon said radiology image data, and generating the set of datarepresenting a geometrical object further based on said CDP, whereinsaid geometrical object is further confined by said CDP.
 7. The methodof claim 1, wherein said radiology image data is at least one of X-ray,ultrasound, computed tomography (CT), nuclear medicine, positronemission tomography (PET) or magnetic resonance imaging (MRI).
 8. Asystem for generating a three dimensional representation of a joint, thesystem comprising: a memory; a communications interface; and a processorconfigured to perform the steps of: receiving radiology image datarepresenting images of a joint in three dimensions; obtaining an initialsegmentation process control parameter set as a previously used andstored trained segmentation process control parameter set; generating afirst three dimensional representation based on the trained segmentationprocess control parameter set; evaluating said generated first threedimensional representation by generating a quality value; modifying thetrained segmentation process control parameter set based on said qualityvalue in order to iterate the process until said quality value exceeds apredefined threshold value; and generating a three dimensionalrepresentation of a first surface of the joint based on radiology imagedata and the trained segmentation process control parameter set whensaid quality value exceeds the predefined threshold value.
 9. The systemof claim 8, wherein the processor is further configured to perform thestep of generating a set of data representing a geometrical object basedon said first surface, wherein said geometrical object is confined bysaid first surface.
 10. The system of claim 8, wherein the processor isfurther configured to perform the step of generating a second threedimensional representation of a second surface of the joint in atrainable dynamical model process dependent on a trained dynamical modelprocess control parameter set and said radiology image data.
 11. Thesystem of claim 10, wherein the processor is further configured toperform the step of generating the set of data representing thegeometrical object based on said first surface and said second surface,so that said geometrical object is confined by said first surface andsaid second surface.
 12. The system of claim 10, wherein the firstsurface of the joint is the underlying bone, and the second surface ofthe joint is aligned to undamaged cartilage tissue outer surface. 13.The system of claim 9, wherein the processor is further configured toperform the steps of generating a cartilage damage perimeter (CDP) basedon said radiology image data, and generating the set of datarepresenting a geometrical object further based on said CDP, whereinsaid geometrical object is further confined by said CDP.
 14. The systemof claim 8, wherein said radiology image data is at least one of X-ray,ultrasound, computed tomography (CT), nuclear medicine, positronemission tomography (PET) or magnetic resonance imaging (MRI).